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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCNWYCdXcgrg"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "In this notebook, we are going to run `TapasForSequenceClassification`, a PyTorch/Transformers implementation of the [Tapas algorithm](https://arxiv.org/abs/2004.02349) by Google AI on the test set of [TabFact](https://github.com/wenhuchen/Table-Fact-Checking), a large dataset for table entailment (which is included in the HuggingFace [datasets library](https://github.com/huggingface/datasets)). In this way, we can verify whether our implementation is consistent with the results reported in the paper.\n",
        "\n",
        "* Paper (which is a follow-up on the original TAPAS paper): https://arxiv.org/abs/2010.00571\n",
        "* Tabfact paper: https://arxiv.org/abs/1909.02164\n",
        "\n",
        "Note that `TapasForSequenceClassification` is really similar to `BertForSequenceClassification` (i.e. it adds a linear layer on top of the pooled output). The difference between the two is that for TAPAS, you need to use `TapasTokenizer` to prepare table-question pairs for the model instead of regular sequences."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VB5aw-Exy8H"
      },
      "source": [
        "## Setting up environment\n",
        "\n",
        "Make sure to set runtime to GPU.\n",
        "We install the `transformers` library from source, as well as the soft dependency on `torch-scatter`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AhQQH2UyxU0v",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0be09a1-1f45-4126-b173-7f9dabebe579"
      },
      "source": [
        "! rm -r transformers\n",
        "! git clone https://github.com/huggingface/transformers.git\n",
        "! cd transformers\n",
        "! pip install ./transformers"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'transformers': No such file or directory\n",
            "Cloning into 'transformers'...\n",
            "remote: Enumerating objects: 56803, done.\u001b[K\n",
            "remote: Total 56803 (delta 0), reused 0 (delta 0), pack-reused 56803\u001b[K\n",
            "Receiving objects: 100% (56803/56803), 42.23 MiB | 30.18 MiB/s, done.\n",
            "Resolving deltas: 100% (39832/39832), done.\n",
            "Processing ./transformers\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers==4.1.0.dev0) (1.18.5)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers==4.1.0.dev0) (3.0.12)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 12.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers==4.1.0.dev0) (2019.12.20)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers==4.1.0.dev0) (20.7)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers==4.1.0.dev0) (2.23.0)\n",
            "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers==4.1.0.dev0) (0.8)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers==4.1.0.dev0) (4.41.1)\n",
            "Collecting tokenizers==0.9.4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0f/1c/e789a8b12e28be5bc1ce2156cf87cb522b379be9cadc7ad8091a4cc107c4/tokenizers-0.9.4-cp36-cp36m-manylinux2010_x86_64.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 47.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==4.1.0.dev0) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==4.1.0.dev0) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==4.1.0.dev0) (0.17.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers==4.1.0.dev0) (2.4.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==4.1.0.dev0) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==4.1.0.dev0) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==4.1.0.dev0) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==4.1.0.dev0) (3.0.4)\n",
            "Building wheels for collected packages: transformers\n",
            "  Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-4.1.0.dev0-cp36-none-any.whl size=1504632 sha256=8705bc181bf94d3c6b24151d0ec44c24b29b1ada9f6ae66394cb73f0e63c624b\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-3ailijku/wheels/23/19/dd/2561a4e47240cf6b307729d58e56f8077dd0c698f5992216cf\n",
            "Successfully built transformers\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893261 sha256=efb1404774a2919352b108e7e84695d08ef7796cfca348d732c49f4d71591f52\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses, tokenizers, transformers\n",
            "Successfully installed sacremoses-0.0.43 tokenizers-0.9.4 transformers-4.1.0.dev0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAV142ECxhWQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f176af9b-e564-4d7c-b49d-41ed0162c026"
      },
      "source": [
        "! pip install torch-scatter==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.7.0.html"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Looking in links: https://pytorch-geometric.com/whl/torch-1.7.0.html\n",
            "Collecting torch-scatter==latest+cu101\n",
            "\u001b[?25l  Downloading https://pytorch-geometric.com/whl/torch-1.7.0/torch_scatter-latest%2Bcu101-cp36-cp36m-linux_x86_64.whl (11.9MB)\n",
            "\u001b[K     |████████████████████████████████| 11.9MB 269kB/s \n",
            "\u001b[?25hInstalling collected packages: torch-scatter\n",
            "Successfully installed torch-scatter-2.0.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fU_6FerLp1Lb"
      },
      "source": [
        "We also install the datasets library from source:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8MelyhHo0fP",
        "outputId": "933f3f7f-f96a-4c91-c21a-bedb61cfa919"
      },
      "source": [
        "! rm -r datasets\r\n",
        "! git clone https://github.com/huggingface/datasets.git\r\n",
        "! cd datasets\r\n",
        "! pip install ./datasets"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'datasets': No such file or directory\n",
            "Cloning into 'datasets'...\n",
            "remote: Enumerating objects: 31, done.\u001b[K\n",
            "remote: Counting objects: 100% (31/31), done.\u001b[K\n",
            "remote: Compressing objects: 100% (24/24), done.\u001b[K\n",
            "remote: Total 24887 (delta 9), reused 15 (delta 7), pack-reused 24856\u001b[K\n",
            "Receiving objects: 100% (24887/24887), 39.59 MiB | 36.26 MiB/s, done.\n",
            "Resolving deltas: 100% (9149/9149), done.\n",
            "Processing ./datasets\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.6/dist-packages (from datasets==1.1.3) (1.18.5)\n",
            "Collecting pyarrow>=0.17.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d7/e1/27958a70848f8f7089bff8d6ebe42519daf01f976d28b481e1bfd52c8097/pyarrow-2.0.0-cp36-cp36m-manylinux2014_x86_64.whl (17.7MB)\n",
            "\u001b[K     |████████████████████████████████| 17.7MB 218kB/s \n",
            "\u001b[?25hRequirement already satisfied: dill in /usr/local/lib/python3.6/dist-packages (from datasets==1.1.3) (0.3.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from datasets==1.1.3) (1.1.5)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.6/dist-packages (from datasets==1.1.3) (2.23.0)\n",
            "Requirement already satisfied: tqdm<4.50.0,>=4.27 in /usr/local/lib/python3.6/dist-packages (from datasets==1.1.3) (4.41.1)\n",
            "Collecting xxhash\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f7/73/826b19f3594756cb1c6c23d2fbd8ca6a77a9cd3b650c9dec5acc85004c38/xxhash-2.0.0-cp36-cp36m-manylinux2010_x86_64.whl (242kB)\n",
            "\u001b[K     |████████████████████████████████| 245kB 54.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: multiprocess in /usr/local/lib/python3.6/dist-packages (from datasets==1.1.3) (0.70.11.1)\n",
            "Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from datasets==1.1.3) (0.8)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->datasets==1.1.3) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->datasets==1.1.3) (2.8.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->datasets==1.1.3) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->datasets==1.1.3) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->datasets==1.1.3) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->datasets==1.1.3) (3.0.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas->datasets==1.1.3) (1.15.0)\n",
            "Building wheels for collected packages: datasets\n",
            "  Building wheel for datasets (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for datasets: filename=datasets-1.1.3-cp36-none-any.whl size=158096 sha256=647eb87e3ad8c5281854b6a262eb8e932d8182c728d0b3c6574a43ae3a0f67d8\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ax5338_u/wheels/d7/33/59/8f64453f60990c3158cc14272127d74a1f77d9919b010387d1\n",
            "Successfully built datasets\n",
            "Installing collected packages: pyarrow, xxhash, datasets\n",
            "  Found existing installation: pyarrow 0.14.1\n",
            "    Uninstalling pyarrow-0.14.1:\n",
            "      Successfully uninstalled pyarrow-0.14.1\n",
            "Successfully installed datasets-1.1.3 pyarrow-2.0.0 xxhash-2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dv2DFfBMyuke"
      },
      "source": [
        "## Loading the model\n",
        "\n",
        "Here we load in a base-sized TAPAS model, which was fine-tuned on TabFact, and move it to GPU (if available):\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yd-2M_-hyuJB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "9a7e2ff0396b41eea78252e24b75dded",
            "786d5fd494404ec7b8883177828ef449",
            "9f0799229ca447f4b725cff7c54e5518",
            "14eea81bdabd47809c573b9ba7a185fd",
            "3bebeaa123524f7d8a6acfa8ad6dde71",
            "fc791ab1bf04446294648c45a3a65b54",
            "f67c343145b743a1bc436f08fe2380f6",
            "65dc714e12f841eab9ea09d55c3ad4f3",
            "46c9f0d44632476db16efcadc1098e98",
            "8e44f13aad8e41d2b56d7099151b81c4",
            "a272bcf9dae54b72bff5ae99a4ab5e3d",
            "87fec1a5c2354ac1a60bb1a82e9416df",
            "6de3b6b0cd4649b391adbc2181bbf31a",
            "3e44e3afeece4ca3a2bae5c9e154a8e4",
            "d9c25d7fed8943c5a9e00fa5739618ce",
            "4de25fbba4dc46ce846ebfafd3665918"
          ]
        },
        "outputId": "2f59cf4b-7e4c-4807-f3e3-ae63e23d3fdf"
      },
      "source": [
        "from transformers import TapasForSequenceClassification\n",
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model = TapasForSequenceClassification.from_pretrained(\"google/tapas-base-finetuned-tabfact\")\n",
        "model.to(device)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a7e2ff0396b41eea78252e24b75dded",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1452.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "46c9f0d44632476db16efcadc1098e98",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=442778215.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TapasForSequenceClassification(\n",
              "  (tapas): TapasModel(\n",
              "    (embeddings): TapasEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(1024, 768)\n",
              "      (token_type_embeddings_0): Embedding(3, 768)\n",
              "      (token_type_embeddings_1): Embedding(256, 768)\n",
              "      (token_type_embeddings_2): Embedding(256, 768)\n",
              "      (token_type_embeddings_3): Embedding(2, 768)\n",
              "      (token_type_embeddings_4): Embedding(256, 768)\n",
              "      (token_type_embeddings_5): Embedding(256, 768)\n",
              "      (token_type_embeddings_6): Embedding(10, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.07, inplace=False)\n",
              "    )\n",
              "    (encoder): TapasEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): TapasLayer(\n",
              "          (attention): TapasAttention(\n",
              "            (self): TapasSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): TapasSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): TapasIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): TapasOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): TapasLayer(\n",
              "          (attention): TapasAttention(\n",
              "            (self): TapasSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): TapasSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): TapasIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): TapasOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): TapasLayer(\n",
              "          (attention): TapasAttention(\n",
              "            (self): TapasSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): TapasSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): TapasIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): TapasOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): TapasLayer(\n",
              "          (attention): TapasAttention(\n",
              "            (self): TapasSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): TapasSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): TapasIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): TapasOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): TapasLayer(\n",
              "          (attention): TapasAttention(\n",
              "            (self): TapasSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): TapasSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): TapasIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): TapasOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): TapasLayer(\n",
              "          (attention): TapasAttention(\n",
              "            (self): TapasSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): TapasSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): TapasIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): TapasOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): TapasLayer(\n",
              "          (attention): TapasAttention(\n",
              "            (self): TapasSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): TapasSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): TapasIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): TapasOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): TapasLayer(\n",
              "          (attention): TapasAttention(\n",
              "            (self): TapasSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): TapasSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): TapasIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): TapasOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): TapasLayer(\n",
              "          (attention): TapasAttention(\n",
              "            (self): TapasSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): TapasSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): TapasIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): TapasOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): TapasLayer(\n",
              "          (attention): TapasAttention(\n",
              "            (self): TapasSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): TapasSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): TapasIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): TapasOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): TapasLayer(\n",
              "          (attention): TapasAttention(\n",
              "            (self): TapasSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): TapasSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): TapasIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): TapasOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): TapasLayer(\n",
              "          (attention): TapasAttention(\n",
              "            (self): TapasSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): TapasSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.07, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): TapasIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): TapasOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.07, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): TapasPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.07, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IyCbXsO_vio8"
      },
      "source": [
        "## Preparing the data\r\n",
        "\r\n",
        "Here we read in the test set of the TabFact dataset, on which we are going to evaluate the fine-tuned checkpoint."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217,
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            "ec75f0b918904bf792baa393077cba80",
            "6ce4b3827d9f460290cb65cab3effb94",
            "1bee4a748ca34417974381da605c3a41",
            "1b2e461c05a9466ba0cd19b0d3a93a2d",
            "ae052cfc876f4256bd1b5e63109ff205",
            "a3bb2b2cda01473687e1b1370fff0c64",
            "191f673df645473da3af5f00c018344b",
            "95da946aa4554bc39e959767e5b17cbe",
            "d4beda687df447758e7ce4cf72b296d6",
            "f841ead508694af595b8d3571bbde314",
            "d59de350b3aa4645a1bdcb8a640de297",
            "b609f93262d34e8eb03d706e3ab8100c",
            "d4f64d9a52aa419597adad8836c79584",
            "6c81d01ef39147ac9392f194e7056ac8",
            "bf9eaa6b96f6480faad8b57bc17be509",
            "9e71046be526478a81eb13af28989f81",
            "4e93a29161d348ae845ccefebedd9c8e",
            "1df8300c49bd4492baf19161da2b5909",
            "17d679b8c657479bbd629199f9bac5be",
            "de3384d6440f4f14a13a9b57b90d1ef0"
          ]
        },
        "id": "jl_HnBzWpDro",
        "outputId": "27d5cc81-100b-4b53-c481-5bc847b9ef08"
      },
      "source": [
        "from datasets import load_dataset\r\n",
        "\r\n",
        "dataset = load_dataset('tab_fact', 'tab_fact', split='test')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c9ee6d127fde42b0b23d1d55a5cb107d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=2221.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8b09a04dd514bd39343725cd09bb9ac",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1340.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Downloading and preparing dataset tab_fact/tab_fact (download: 187.41 MiB, generated: 121.30 MiB, post-processed: Unknown size, total: 308.71 MiB) to /root/.cache/huggingface/datasets/tab_fact/tab_fact/1.0.0/bd64c4ee1b4127f8377f1817669219ec36aaf65cb8c78d7c995902e25ef362b6...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bd870bff7cb749d5ba8d0aba2ac7ec44",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Downloading', max=1.0, style=ProgressSt…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fc4fb0ca4bdb41b6ad931db8dbc78a17",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae052cfc876f4256bd1b5e63109ff205",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d4f64d9a52aa419597adad8836c79584",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset tab_fact downloaded and prepared to /root/.cache/huggingface/datasets/tab_fact/tab_fact/1.0.0/bd64c4ee1b4127f8377f1817669219ec36aaf65cb8c78d7c995902e25ef362b6. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0I4tL_UYLAl",
        "outputId": "be89b060-7e54-4530-b22b-0b7a7c08afd1"
      },
      "source": [
        "dataset.column_names"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['id', 'label', 'statement', 'table_caption', 'table_id', 'table_text']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pazQrL7ARr63"
      },
      "source": [
        "Each example in the TabFact dataset is a statement about a table, and the label indicates whether the statement is supported (1) or refuted (0) by the contents of the table. So it's a binary classification problem. \r\n",
        "\r\n",
        "Let's visualize an example:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "id": "QttqcI1Zthwd",
        "outputId": "fc85b074-ce0f-4616-d194-c6220ec08e47"
      },
      "source": [
        "import pandas as pd\r\n",
        "\r\n",
        "# let's take a random example\r\n",
        "example = dataset[0]\r\n",
        "id2label = {0: \"REFUTES\", 1: \"SUPPORTS\"}\r\n",
        "\r\n",
        "data = example['table_text']\r\n",
        "\r\n",
        "# convert table_text into a Pandas dataframe\r\n",
        "table = pd.DataFrame([x.split('#') for x in data.split('\\n')[1:-1]], columns=[x for x in data.split('\\n')[0].split('#')])\r\n",
        "display(table)\r\n",
        "print(\"\")\r\n",
        "print(\"Statement:\", example['statement'])\r\n",
        "print(\"Label:\", id2label[example['label']])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tournament</th>\n",
              "      <th>wins</th>\n",
              "      <th>top - 5</th>\n",
              "      <th>top - 10</th>\n",
              "      <th>top - 25</th>\n",
              "      <th>events</th>\n",
              "      <th>cuts made</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>masters tournament</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>us open</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>the open championship</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>pga championship</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>totals</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>18</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              tournament wins top - 5 top - 10 top - 25 events cuts made\n",
              "0     masters tournament    0       1        2        4      4         4\n",
              "1                us open    0       2        3        4      6         5\n",
              "2  the open championship    1       2        2        2      3         3\n",
              "3       pga championship    0       0        1        2      5         4\n",
              "4                 totals    1       5        8       12     18        16"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Statement: tony lema be in the top 5 for the master tournament , the us open , and the open championship\n",
            "Label: SUPPORTS\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fS7BR4txZ1m"
      },
      "source": [
        "We write the logic to turn the `table_text` column into a Pandas dataframe into a function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RG-2KmbRw_dn"
      },
      "source": [
        "def read_text_as_pandas_table(table_text: str):\r\n",
        "    table = pd.DataFrame([x.split('#') for x in table_text.split('\\n')[1:-1]], columns=[x for x in table_text.split('\\n')[0].split('#')]).fillna('')\r\n",
        "    table = table.astype(str)\r\n",
        "    return table"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLoEnZtUY1mp"
      },
      "source": [
        "Let's check if TapasTokenizer can prepare the data correctly:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102,
          "referenced_widgets": [
            "7e8ff394421f4f0eae05ff88d124aaf1",
            "0796890d8d2e466baa2f4d47ba1ef118",
            "2a05fe43f6664d3eb984cccd76c414d3",
            "48f95b8cf0ca45aa9068bde4a1dc5e38",
            "5b70a28bd1c64fabb9f208d814ea7aa5",
            "adb6cc8c58ae47a69de0e84b70e71628",
            "c70520e067884742abc59569dfbd6904",
            "0da52abfbeff4bb6a74e7b2b635464c7"
          ]
        },
        "id": "4p-RUk1NM_32",
        "outputId": "893e6d84-e24c-4664-8b0a-a137711f2a99"
      },
      "source": [
        "from transformers import TapasTokenizer\r\n",
        "\r\n",
        "tokenizer = TapasTokenizer.from_pretrained(\"google/tapas-base-finetuned-tabfact\")\r\n",
        "\r\n",
        "# test on a random example\r\n",
        "example = dataset[0]\r\n",
        "inputs = tokenizer(table=read_text_as_pandas_table(example['table_text']),\r\n",
        "                   queries=example['statement'],\r\n",
        "                   padding='max_length')\r\n",
        "inputs"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e8ff394421f4f0eae05ff88d124aaf1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=262028.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9Hf19fZY6qY"
      },
      "source": [
        "Now let's use the `.map()` functionality of `datasets` to tokenize and prepare for the model the entire test split of the dataset. Note that we tokenize each table-question pair independently (we don't set `batched=True`): "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "5a2b749b94fc4fd7aa022661248ee062",
            "aa31e09cca9a416f857c551bb9544fad",
            "2cda84ca565a4e86842f172f49180736",
            "ad0d36cbdd224fff93b41950ad187eb2",
            "80d76fdf3ca54c418dfd34f5fba9330a",
            "0784a7fbcf7a459db2a1c7f128ec3db1",
            "f6626e8d54d743aab888a79982016088",
            "57eb080ac8bb439bae7022e4c3c6884c"
          ]
        },
        "id": "DafZ9NpQweNX",
        "outputId": "a93ed78d-2d8e-4abf-deb7-446b9f959599"
      },
      "source": [
        "from datasets import Features, Sequence, ClassLabel, Value, Array2D\r\n",
        "\r\n",
        "# we need to define the features ourselves as the token_type_ids of TAPAS are different from those of BERT \r\n",
        "features = Features({\r\n",
        "    'attention_mask': Sequence(Value(dtype='int64')),\r\n",
        "    'id': Value(dtype='int32'),\r\n",
        "    'input_ids': Sequence(feature=Value(dtype='int64')),\r\n",
        "    'label': ClassLabel(names=['refuted', 'entailed']),\r\n",
        "    'statement': Value(dtype='string'),\r\n",
        "    'table_caption': Value(dtype='string'),\r\n",
        "    'table_id': Value(dtype='string'),\r\n",
        "    'table_text': Value(dtype='string'),\r\n",
        "    'token_type_ids': Array2D(dtype=\"int64\", shape=(512, 7))\r\n",
        "})\r\n",
        "test = dataset.map(\r\n",
        "    lambda e: tokenizer(table=read_text_as_pandas_table(e['table_text']), queries=e['statement'], \r\n",
        "                                       truncation=True,\r\n",
        "                                       padding='max_length'),\r\n",
        "    features=features\r\n",
        ")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5a2b749b94fc4fd7aa022661248ee062",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=12779.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fAfbFhN3ZAtk"
      },
      "source": [
        "Let's create a PyTorch dataloader based on this:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZhd56Gg-j89"
      },
      "source": [
        "# map to PyTorch tensors and only keep columns we need\r\n",
        "test.set_format(type='torch', columns=['input_ids', 'attention_mask', 'token_type_ids', 'label'])\r\n",
        "# create PyTorch dataloader\r\n",
        "test_dataloader = torch.utils.data.DataLoader(test, batch_size=4)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-fjBqOGGboF"
      },
      "source": [
        "We can verify whether everything is created correctly, for example by verifying their shapes and decoding the `input_ids` of the first example of the first batch:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "id": "bjxLr_NuGh3m",
        "outputId": "2edef04b-9f7e-4f4a-a9b5-c1ef9f87b104"
      },
      "source": [
        "# let's check the first batch\r\n",
        "batch = next(iter(test_dataloader))\r\n",
        "assert batch[\"input_ids\"].shape == (4, 512)\r\n",
        "assert batch[\"attention_mask\"].shape == (4, 512)\r\n",
        "assert batch[\"token_type_ids\"].shape == (4, 512, 7)\r\n",
        "tokenizer.decode(batch[\"input_ids\"][0])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:850: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.tensor(x, **format_kwargs)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'[CLS] tony lema be in the top 5 for the master tournament, the us open, and the open championship [SEP] tournament wins top - 5 top - 10 top - 25 events cuts made masters tournament 0 1 2 4 4 4 us open 0 2 3 4 6 5 the open championship 1 2 2 2 3 3 pga championship 0 0 1 2 5 4 totals 1 5 8 12 18 16 [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZZdJRA0x18e"
      },
      "source": [
        "## Run evaluation\r\n",
        "\r\n",
        "Now we can compute the accuracy of TAPAS on the test set of TabFact! Incredible how easy 🤗 datasets makes this!\r\n",
        "\r\n",
        "Note that this will take a couple of minutes. We set the batch size to only 4, to make sure a single GPU on Google Colab can handle this. You can increase it of course."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yu67t2Nx21s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7359099-b3e1-4df2-ee9d-9a57a63cae26"
      },
      "source": [
        "from datasets import load_metric\n",
        "\n",
        "accuracy = load_metric(\"accuracy\")\n",
        "\n",
        "print(\"Starting evaluation...\")\n",
        "number_processed = 0\n",
        "total = len(test_dataloader) * batch[\"input_ids\"].shape[0] # number of batches * batch_size\n",
        "\n",
        "for batch in test_dataloader:\n",
        "    # get the inputs\n",
        "    input_ids = batch[\"input_ids\"].to(device)\n",
        "    attention_mask = batch[\"attention_mask\"].to(device)\n",
        "    token_type_ids = batch[\"token_type_ids\"].to(device)\n",
        "    labels = batch[\"label\"].to(device)\n",
        "\n",
        "    # forward pass\n",
        "    outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels)\n",
        "    model_predictions = outputs.logits.argmax(-1)\n",
        "\n",
        "    # add metric\n",
        "    accuracy.add_batch(predictions=model_predictions, references=labels)\n",
        "\n",
        "    number_processed += batch[\"input_ids\"].shape[0]\n",
        "    print(f\"Processed {number_processed} / {total} examples\")\n",
        "\n",
        "final_score = accuracy.compute()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting evaluation...\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ibUQ1P5CaJ2B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "099e1b42-d846-483b-bd99-13c1ba90942f"
      },
      "source": [
        "print(final_score)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'accuracy': 0.7711871038422412}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uupxYz6yApLh"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}